(This is a preprint that is currently under review)
We present an easy-to-use, nonlinear filter for effective background identification in fluorescence microscopy images with dense and low-contrast foreground. The pixel-wise filtering is based on comparison of the pixel intensity with the mean intensity of pixels in its local neighborhood. The pixel is given a background or foreground label depending on whether its intensity is less than or greater than the mean respectively. Multiple labels are generated for the same pixel by computing mean expression values by varying neighborhood size. These labels are accumulated to decide the final pixel label. We demonstrate that the performance of our filter favorably compares with state-of-the-art image processing, machine learning, and deep learning methods. We present three use cases that demonstrate its effectiveness, and also show how it can be used in multiplexed fluorescence imaging contexts and as a denoising step in image segmentation. A fast implementation of the filter is available in Python 3 on GitHub.
Multiplexed imaging technologies have made it possible to interrogate complex tumor microenvironments at sub-cellular resolution within their native spatial context. However, proper quantification of this complexity requires the ability to easily and accurately segment cells into their sub-cellular compartments. Within the supervised learning paradigm, deep learning based segmentation methods demonstrating human level performance have emerged. However, limited work has been done in developing such generalist methods within the label-free unsupervised context. Here we present an unsupervised segmentation (UNSEG) method that achieves deep learning level performance without requiring any training data. UNSEG leverages a Bayesian-like framework and the specificity of nucleus and cell membrane markers to construct an a posteriori probability estimate of each pixel belonging to the nucleus, cell membrane, or background. It uses this estimate to segment each cell into its nuclear and cell-membrane compartments. We show that UNSEG is more internally consistent and better at generalizing to the complexity of tissue morphology than current deep learning methods. This allows UNSEG to unambiguously identify the cytoplasmic compartment of a cell, which we employ to demonstrate its use in an exemplar biological scenario. Within the UNSEG framework, we also introduce a new perturbed watershed algorithm capable of stably and automatically segmenting a cluster of cell nuclei into individual cell nuclei that increases the accuracy of classical watershed. Perturbed watershed can also be used as a standalone algorithm that researchers can incorporate within their supervised or unsupervised learning approaches to extend classical watershed, particularly in the multiplexed imaging context. Finally, as part of developing UNSEG, we have generated a high-quality annotated gastrointestinal tissue (GIT) dataset, which we anticipate will be useful for the broader research community. We demonstrate the efficacy of UNSEG on the GIT dataset, publicly available datasets, and on a range of practical scenarios. In these contexts, we also discuss the possibility of bias inherent in quantification of segmentation accuracy based on F1 score. Segmentation, despite its long antecedents, remains a challenging problem, particularly in the context of tissue samples. UNSEG, an easy-to-use algorithm, provides an unsupervised approach to overcome this bottleneck, and as we discuss, can help improve deep learning based segmentation methods by providing a bridge between unsupervised and supervised learning paradigms. (GitHub)
PARP2 is a DNA-dependent ADP-ribosyl transferase (ARTs) enzyme with Poly(ADP-ribosyl)ation activity that is triggered by DNA breaks. It plays a role in the Base Excision Repair pathway, where it has overlapping functions with PARP1. However, additional roles for PARP2 have emerged in the response of cells to replication stress. In this study, we demonstrate that PARP2 promotes replication stress-induced telomere fragility and prevents telomere loss following chronic induction of oxidative DNA lesions and BLM helicase depletion. Telomere fragility results from the activity of the break-induced replication pathway (BIR). During this process, PARP2 promotes DNA end resection, strand invasion and BIR-dependent mitotic DNA synthesis by orchestrating POLD3 recruitment and activity. Our study has identified a role for PARP2 in the response to replication stress. This finding may lead to the development of therapeutic approaches that target DNA-dependent ART enzymes, particularly in cancer cells with high levels of replication stress.
Significance: Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states.
Aim: This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection.
Approach: We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI’s applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging.
Results: QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research.
Conclusions: QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI’s capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.
Introduction: Lynch syndrome (LS) is the most common hereditary cause of colorectal cancer (CRC), increasing lifetime risk of CRC by up to 70%. Despite this higher lifetime risk, disease penetrance in LS patients is highly variable and most LS patients undergoing CRC surveillance will not develop CRC. Therefore, biomarkers that can correctly and consistently predict CRC risk in LS patients are needed to both optimize LS patient surveillance and help identify better prevention strategies that reduce risk of CRC development in the subset of high-risk LS patients.
Methods: Normal-appearing colorectal tissue biopsies were obtained during repeat surveillance colonoscopies of LS patients with and without a history of CRC, healthy controls (HC), and patients with a history of sporadic CRC. Biopsies were cultured in an ex-vivo explant system and their supernatants were assayed via multiplexed ELISA to profile the local immune signaling microenvironment. High quality cytokines were identified using the rxCOV fidelity metric. These cytokines were used to perform elastic-net penalized logistic regression-based biomarker selection by computing a new measure – overall selection probability – that quantifies the ability of each marker to discriminate between patient cohorts being compared.
Results: Our study demonstrated that cytokine based local immune microenvironment profiling was reproducible over repeat visits and sensitive to patient LS-status and CRC history. Furthermore, we identified sets of cytokines whose differential expression was predictive of LS-status in patients when compared to sporadic CRC patients and in identifying those LS patients with or without a history of CRC. Enrichment analysis based on these biomarkers revealed an LS and CRC status dependent constitutive inflammatory state of the normal appearing colonic mucosa.
Discussion: This prospective pilot study demonstrated that immune profiling of normal appearing colonic mucosa discriminates LS patients with a prior history of CRC from those without it, as well as patients with a history of sporadic CRC from HC. Importantly, it suggests the existence of immune signatures specific to LS-status and CRC history. We anticipate that our findings have the potential to assess CRC risk in individuals with LS and help in preemptively mitigating it by optimizing surveillance and identifying candidate prevention targets. Further studies are required to validate our findings in an independent cohort of LS patients over multiple visits.
